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metadata
tags:
  - summarization
  - ar
  - seq2seq
  - mbart
  - Abstractive Summarization
  - generated_from_trainer
datasets:
  - xlsum
model-index:
  - name: mbart-finetune-ar-xlsum
    results: []

mbart-finetune-ar-xlsum

This model is a fine-tuned version of facebook/mbart-large-50 on the xlsum dataset. It achieves the following results on the evaluation set:

  • Loss: 4.4328
  • Rouge-1: 15.56
  • Rouge-2: 4.64
  • Rouge-l: 13.59
  • Gen Len: 38.86
  • Bertscore: 71.53

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0005
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 250
  • num_epochs: 5
  • label_smoothing_factor: 0.1

Training results

Framework versions

  • Transformers 4.20.0
  • Pytorch 1.11.0+cu113
  • Datasets 2.3.2
  • Tokenizers 0.12.1